{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "biblical-chrome",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "<img src =\"https://annual.ametsoc.org/themes/annual/images/AMS22_Logo_300.png\" width=25% alt=\"American Meteorological Society logo\">\n",
    "<img src=\"../logos/unidata_logo_horizontal.png\" width=25% alt=\"Unidata logo\">\n",
    "</div>\n",
    "\n",
    "# Python Workshop\n",
    "### AMS Student Conference 2022\n",
    "\n",
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Note</p>\n",
    "    If running this notebook on a local computer, the <code>pyaos-ams-2022</code> conda environment must be activated.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "native-librarian",
   "metadata": {},
   "source": [
    "## Table of Contents <a class=\"anchor\" id=\"top\"></a>\n",
    "\n",
    "* [Objective](#objective)\n",
    "* [Strategy](#strategy)\n",
    "* [Step 0: Import required packages](#step0)\n",
    "* [Step 1: Browse the THREDDS Data Server (TDS)](#step1)\n",
    "* [Step 2: Model output, equivalent potential temperature ($\\theta_e$)](#step2)\n",
    "    * [Step 2a: Obtain Model Output](#step2a)\n",
    "    * [Step 2b: Prepare Model Output](#step2b)\n",
    "    * [Step 2c: Visualize Model Output](#step2c)"
   ]
  },
  {
   "attachments": {
    "e141bb24-2e9b-415e-a737-cab827f14c63.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "corrected-injection",
   "metadata": {},
   "source": [
    "## Objective\n",
    "Create a color-filled contour plot of equivalent potential temperature ($\\theta_e$) on geographic axes.\n",
    "\n",
    "![AMS2022SC_final_output.png](attachment:e141bb24-2e9b-415e-a737-cab827f14c63.png)"
   ]
  },
  {
   "attachments": {
    "d2d922f4-2adc-4597-8a32-3de125a0efb0.png": {
     "image/png": 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j/Czwd4xZE2+ER/PkJMVI5VulHxQeJg37pSNR642/1ATrdFS0L3YU4ei4zThCi2VuOEttt7vJw52EicPHCgjNxESy8wOcQeEB3JUnvn6tusbQ4s3waJ6cRMBJQ8JjxxkKbdoWHeH2yvAop0uQDg8t+uiPy5VO0iz6yPBok35AeAAf6aG3ngeQLr3W+k4vzwO+LLTIr8qrxUq72wcLEY93krVMJyC+4PjBM+SNw6PalahPlSR3NOEwq/o7QzDk6OdBvsGcsoaK+1uBk4aERzhowm5/pzNmZhXhYdFhOwOf2oxe4bFHYfY27BKL8LAu2CHH20PDo+21kgPCQwkjpHc+mRDJcbT9i50Ynr8uG3mozThCq/FlB+g2wdiwKHKUsLPP3lc+H+8kGzNl4+JmnuycwsImCo9aVyLjuAZGh5m1XPApDLdWqUWHjdTGbFt1fyvo45DwsD5ioPJttHbu4Tk4GLhw2rzgkAsD2zqMdbfC48hS1nxcEkfFoQ/vfKHVca0C0dFXD0Yq3/HSVa29RdzDkI7ONCwPUwtPMDy38so7IzaO8CNs+tUN5P6whBfM2Xdx0neLxzsJhxXFUAzN9KOtLQbNBwaxtZ/hYZllV6I+RXlW1wkfOpKt4W8YtzoOGoPhJYIbmh82qiq4vxV0clB4WHXAOsVv4NcXtHCxBomTbIsrNhj87o4CvcXWKOvjTkFpv1hBaN+Lccq3Ct/XSX65hH3ERRiAwA7XS6wTSmCwF51fdxnHKNNswrGVH6ro4GIbGXiBNE9wkjWN34WmDARCjyltUaIEuhIZh8N2jBClMLZtf+Jgv1wSBp/VrYS/RDCYx+7vAr0cFh54xeYW8Wf1ng54Z79nox9+8RmXm7m2R3jwhZ0nKMyhFb1j046q3lF19YWcvRilfLvwI8OD3lm/+dXX6HeP4o2QHcYVjmYN/+xq79g8bVdbntyal6IXct7gCU7a6yQ6Do/12YTeafe/c3fYJFJ25XZ40DPh/D7ibF6y4QU1Ku/YtE2s9zg5rL6Qsw10c2B4zJ8xyncIP52TOChmwKydNBfQzxweJV3C5/CI+H4nzQV0NIdHQafwUzkJR0SGlp7JjJ00H9DTHB6BbuGzkwqW5qSsvEgIn50UWJyTsvJOSvjsJLE8Jy1G+f924KWSwmcneakFOmkxynfBQmnhs5NYaIlOysobN4TPTkKZRTppGcp34Y+PvSV8dpIVWaaTsvK3hc9OWqyTsvK3hc9OWqyTFq98D+GzkxbrpMUr38c+O2mpTsrK9zDPTlqqk9D1V0N69QDKK5nCq30t1LUeLNlJ+n4ppFcPTHmlkqjel0Jd60F20lL5tXQH9CE7aan80ncmQXZSZpH8aj7rMJO5gzySMplFYefcOegzE5BH0hLR9avMzJFc04LfMyYLejYzM3sk17TkkbRAslA/BMXVtDDkpwp6ryszdyTXtOSRtEBcKN1/lZkllEhxNS0K+WmCnhWpyZlZQokk17TkkbQsKFEWav5QIsXVtBQhP0XQsxo1OTNLKJHkmpY8kpYFJcpCzR9KpLiaFob8H/yNrDE26FmJmpyZJZRIck1LHknLghJloeYPJVJcTYuHvD+exxgX9KxCTc7MEkokuaYlj6RlQYmyUPOHEimupiWE/CRBzwrY4MxMoUSSa1rySFoWlCgLNX8okeJqWsqQnyDoWdzryswTSiS5piWPpGVBibJQ84cSKa7S0HIoCvnRQc/CqiozSyiR5EpDy6HkkbQYKFEWav5QIsVVGloOpQj5kUHPoqooM0sokeRKQ8uh5JG0GChRFmr+UCLFVRpa6g2Z/dFmyIigZ0FVk5kllEhypaGlxkd/tBmSR9IrQ4myUPOHEimu0tBSpe7l7qBnMVWSmSWUSHKloaVK3UseSa8LJcpCzR9KpLhKQ0uVup87g56FVEVmllAiyZWGlip1P3kkvSqU6MFCva/ISYujOKAmpcdwXLNNOy3OD0qkuEpDS5Uaw11BzyKqYPGcNxxUU4zPCaFEkisNLVVqDPMZSbvviPEgc8su7dSaOxl7bnV10OKjoUTfHfKXPUTb7s++mJw8rrbmTekePHjygFrqxYOhRIqrNLRUqXHcEfQsoOIToKHi7PYXZf8Qek4en2byofQDoESSKw0tVWocjxpJHx7Gq9X6aEsX7HbIu69e6OSxNX8oOSmU6Fsnj3PQE6y154WI7T6Fu59z/I956NaWu9v93VAixVUaWqrUWAYHPc1VeAIqkwcIe4GfAvceSneBgffAg0dKJLnS0FKlxvKYkXRr8vhGnrRrwGZvDR54QMlJoUTfOHlcoeb26mnsCtY8esyTx1AokeIqDS1VajwDg57GKjoNkcuPllz7WPop5Mmj4EEj6cO8WYYxhsxW6e8lTx5Tc0G7y4sNF0wlOPlAl/G/s/n01Yy0Cp7/piWwKa4aldal85RXHIC83drVfBannCukfNSdPcPZM8so2yt8s+3Gk0OJFFdpaKlSUzAo6GmqgtMQB2XYzdLXxtdVQ0Ojgocn4h3ShzFyKGTehdGInUyAx6lEGbdrTnGOhjY+PDc+A9+FKtCjCtrvtRpPAyWSXGloqVJT8JiRBKVCHCI4dbhRerq6iz9vlU0wuqRelAxFUqrE45S4mVE5Iq0LruzKjki7w24ORUPWSPmOKD2gC7yN7cYDoUTfN3nAo7ErEMw4EAj6WCR/Ml20Hxadx/+fELp6Cto4bGN1a9YMPZKOwXHJamcNPEvT2pYvzC4nv+YIiWgYTwolUlyloaVKTcOAoKehik1D7HLsF4LYcDZ4O191TZcXSNcfpsCZF4M3Ht5IIkIuX9cjRoSf+5acMU1stABob9ysuR0ab47Xr4t+SKsd9V2ZHY3LxhCOaBiPhxJJrjS0VKlpeMhIKsMeP6BWf6NsBDCM3z5NzxMPD7RziSVpifk2Vdp3DY3dGWp2M+58tD1u+812RNcj57LkT2DeUOvWSQc4tcHTOqCVbNAwHgAl+r7Jo9FsZcDT5ayCpTAlJCePlgobkReLaMnUL0VQodgW5+LGlmvVt4+QQGovMBZKpLhKQ0uVmoreQU8zFZqGyOUY54VClrYDkWjPAAHLi1o8IeQ0gUQhGs996/vimqos0KfmVirX1vy0VAsFtTGeHjY3AmI4lEhypaGlSk3FA0YSL5VTvubRYz2AKRAnD2NfnE7EkrTGfFOV9l1Dw86OR3g/UDx18IymHAEY5tUj5AroVGHM6aMxeFoGtJIttDe8D5To+yYPeKWIOqB+oMFRlwu1u2LlxPu1Vmt+Vnp6a/JIeA0DJ5pzUVPY8vXjDRqttviMmtPq6C7jSaFEiqs0tFSp6egZ9DRSkWmg5IFww4WBRSUdGlShGEiUZ78QXQcUl8MOA2/Fz2gMIU9JBxk1umVGZeXUguFcVHZ852Hlhp/KM9onjw7j8VAiyZWGlio1Hd8/kqix7XZwcFi7kNwIYNzqFtgUE03n5NGtSuuuoWvqr0wd3FqdZlUCRzLR1ILNhpb2HtCgy3gAlOj7Jg+4JZ5EcdIPOdHl6HJW5PgWb9PTERW/NiIvFhHWSrZQ2xRq4iIuVsRENs0RkjCeFEqkuEpDS5WakH4xTxuVmAa4nGzeK55HlpIOjapQDCTKkrySYd+VmwCNVKzRoEq3zFirpFFOHrzYEMFM0hjCKePxUCLJlYaWKjUh3z+SsMfYlgEd0Qxgmz/2vhsF+iuBWJKySFKVtpo7Jg/WE+0WsbU6zapEbbRgs1wcMqBTxgOgRN83edBPutnKQE/pSiTKC8eutdP0Nqoofvi2dNWvjciLRYS1km1gv18cbPBkF1vmj/yhhtoAbIyQlPGkUCLFVRpaqtRk9L3aQCuVmYaGywUcr6SDQ7I278Ow+J3jqpN+DLlNyMQmUrHWVXMrOD4qdwyompVpu6ScUUhjCKeMx0OJJFcaWqrUZDxkJGGfASc2LubXR9Pnuvy5mGPDV8aSFEXSqrSP05bJI5o63rRvxK6nbYy3waOf4tSW7UCjBg3olPEAKNE3Th7lz4f6tdKvO6DBNmFY9teZtw6UDoYV/hrs8kkvn+jsnRW7HJlRc/OYyYO26wPrxma9GZgP1h/WspPu0YnEp9dxynT6wLpd0nhSKJHiKg0tVWoi+gb8UycPvwKxOWKEnU8f79vVmie3yDWRiiHIU94g5PVz7wdhqVjrqrkdjMgVzpKufi+IV4YtY1ifP6JMhwcgDIUj1yWNx0OJJFcaWqrURDxqJOl6QNhBFtRHE4zWe+Rc6Hz9WsWwtlzfB6lIWpV6zU5j8mCFmq+w0scdm/t+so1fT5+4npTYjfAod3NEQ3TXFepIDeiwB72euO6QNB4AJfrOycNaKAGMN4qpBd/ZksqFyXCPirF+R4HyLzjXOAYE1lkJGGEO50RMFMQgJYT+RtPYhWYWf7hpRJnONdrslmMgYTwllEhxlYaWKjUJ/QN+8o3LoU6hY0P6ELTRPbLAf/RGal8MIQQRiITca09j9Q+ruYviTvHVJgzCUzSso0xRjiFbid1XyngslEhypaGlSk3CA0cSHRj5jD6swB1mtBMywuAwQiyvo5miXRWlIlBzsfcpYb1KF4Tddnn3LVgn7/kv//gxGrvtA9op96C2D4ZTUsb9oUTfO3lkpoASKa7S0FKlJmBIwH/D/mY8iIzJ9rwvACWSXGloqVIT8ONHUqYKJcpCzR9KpLhKQ0uVGs2wgM+Txw+AEkmuNLRUqdG8wEjKVKFEWaj5Q4kUV2loqVIjGRrwswv56AS/ctffkqFEkisNLVVqJD9+JGWaUKIs1PyhRIqrNLRUqVEMD/g8efwAKJHkSkNLlRrFC4ykTBNKlIWaP5RIcZWGlio1gnsCPof8D4ASSa40tFSpEeSR9KJQoizU/KFEiqs0tFSpu7kv4HPI/wAokeRKQ0uVups8kl4WSpSFmj+USHGVhpYqdSf3BnwO+R8AJZJcaWipUneSR9ILQ4myUPOHEimu0tByKH+0GXB/wOeQ/wFQIsmVhpZDySNpMVCiLNT8oUSKqzS0HEoZ8mMCPof8D4ASSa40tBxKHkmLgRJloeYPJVJcpfl7IP+PmkPIjwv4HPI/AEokudJogPQmj6RlQYmyUPOHEimupuUXavaQHxvwOeR/AJRIck1LHknLghJloeYPJVJcTUsR8uMDPof8D4ASSa5pySNpWVCiLNT8oUSKq2lRyE8R8DnkfwCUSHJNSx5Jy4ISZaHmDyVSXE0LQ/4/rH9swOeQ/wFQIsk1LXkkLQtKlIWaP5RIcTUtDHkyOuBzyP8AKJHkmpY8kpYFJXKhMrNHcTUtIeQnCHiFfGb2SK5pySNpgWShfgiKq2nxkJ8k4HPI/xQk17TkkbRAslA/BMXVtCDkJwr4HPI/Bck1LXkkLRDJlVkkv6YL+MyiySMpk1kU/9J3JjOOPJIWx19//Q/nCyonNhhaVgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "b10a5da2-465d-4ad9-a90e-551d7dd7db28",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Strategy <a class=\"anchor\" id=\"strategy\"></a>\n",
    "\n",
    "Creating plots like this using MetPy follows this basic workflow:\n",
    "\n",
    "![level1_2.png](attachment:d2d922f4-2adc-4597-8a32-3de125a0efb0.png)\n",
    "\n",
    "We will use the Real-time Meso-analysis (RTMA) output available on the Unidata-hosted THREDDS Data Server, then calculate $\\theta_e$ from the available variables, and finally plot the field on geographic axes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advisory-tenant",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Step 0: Import required packages <a class=\"anchor\" id=\"step0\"></a>\n",
    "\n",
    "First, we need to import all our required packages. Today we're working with:\n",
    "- cartopy\n",
    "- matplotlib\n",
    "- metpy\n",
    "- siphon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numeric-organic",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# We will use the standard \"plt\" abbreviation to access matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import metpy.calc as mpcalc\n",
    "import metpy.plots as mpplots\n",
    "\n",
    "# Here is where we import the TDSCatalog class from siphon for obtaining our data \n",
    "from siphon.catalog import TDSCatalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addressed-drinking",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Step 1: Browse the THREDDS Data Server (TDS) <a class=\"anchor\" id=\"step1\"></a>\n",
    "\n",
    "The **THREDDS Data Server** provides us with coherent access to a large collection of real-time and archived datasets from a variety of environmental data sources at a number of distributed server sites. \n",
    "You can browse the TDS in your web browser using this link: <a href=\"https://thredds.ucar.edu/\" target =\"blank\">https://thredds.ucar.edu/</a>\n"
   ]
  },
  {
   "attachments": {
    "rtma_workflow.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "direct-article",
   "metadata": {
    "tags": []
   },
   "source": [
    "[Top](#top)\n",
    "\n",
    "## Step 2: Obtain, Prepare, and Visualize an Equivalent Potential Temperature ($\\theta_e$) Field <a class=\"anchor\" id=\"step2\"></a>\n",
    "\n",
    "Now we grab data from the [Real-time Meso-analysis (RTMA)](https://nomads.ncep.noaa.gov/txt_descriptions/RTMA_doc.shtml), which gives us a gridded estimate of the realtime conditions. Our goal is to use MetPy to calculate <a href=\"https://glossary.ametsoc.org/wiki/Equivalent_potential_temperature\" target=\"blank\">equivalent potential temperature ($\\theta_e$)</a>, using the fields we have available in the RTMA. \n",
    "\n",
    "We are using the basic obtain > prepare > visualize workflow to accomplish this task. We can expand our strategy diagram to include more detail as below: \n",
    "\n",
    "<div>\n",
    "<img src=\"attachment:rtma_workflow.png\" width=\"800\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-boost",
   "metadata": {
    "tags": []
   },
   "source": [
    "[Top](#top)\n",
    "\n",
    "### Step 2a: Obtain Model Output <a class=\"anchor\" id=\"step2a\"></a>\n",
    "\n",
    "We will use siphon's TDSCatalog object to create a catalog of satellite data from the TDS that match our criteria (RTMA from NCEP, over CONUS). Then we use siphon's remote_access method to read in the latest dataset from the catalog into an `xarray.Dataset`.\n",
    "\n",
    "Notice the use of the `squeeze()` method! Our provided dataset might have stray dimensions that we don't really benefit from keeping around. Feel free to compare what this dataset looks like with and without using this.\n",
    "\n",
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Note</p>\n",
    "    Notice the URL in the TDSCatalog input ends with .xml rather than .html. While your web browser uses html to show you the data in a human-friendly webpage, siphon requires an xml document to create the TDSCatalog object.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "weighted-parks",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create TDS catalog object\n",
    "rtma_cat = TDSCatalog('https://thredds.ucar.edu/thredds/catalog/grib/NCEP/RTMA/CONUS_2p5km/catalog.xml')\n",
    "\n",
    "# Using xarray, we pull the data from the TDS into an xarray DataSet object\n",
    "rtma_data = rtma_cat.datasets['Latest Collection for Real Time Mesoscale Analysis 2.5 km'].remote_access(use_xarray=True)\n",
    "rtma_data = rtma_data.metpy.parse_cf().squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "further-tobago",
   "metadata": {
    "tags": []
   },
   "source": [
    "[Top](#top)\n",
    "\n",
    "### Step 2b: Prepare Model Output and Calculate $\\theta_e$ <a class=\"anchor\" id=\"step2b\"></a>\n",
    "\n",
    "Before we calculate a derived variable such as $\\theta_e$, we need to look up the required variables to calculate that variable. Looking at the metpy.calc documentation for <a href =\"https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.equivalent_potential_temperature.html#metpy.calc.equivalent_potential_temperature\" target=\"blank\">equivalent potential temperature</a>, we can see that this function requires three variables: **surface pressure**, **temperature**, and **dewpoint**. Each of these variables are contained in the rtma_data Dataset, but we need to know the name of each variable as it is stored in the Dataset. \n",
    "\n",
    "\n",
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Find RTMA Variable Names</p>\n",
    "    <code>rtma_data</code> is an xarray Dataset containing several variables. Use the cell below to programmatically display the list of all variables in the Dataset, then find the names of the <b>surface pressure</b>, <b>temperature</b>, and <b>dewpoint</b> variables. \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "compatible-utility",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Find RTMA Variable Names\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closing-induction",
   "metadata": {},
   "source": [
    "Surface pressure is stored in the *\"Pressure_Analysis_surface\"* variable name, which we'll use to select the corresponding DataArray within. Note that creating a new variable is not *necessary*, but can save you some time later and make your code more readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "muslim-nomination",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the surface pressure DataArray to variable `pres`\n",
    "pres = rtma_data.Pressure_Analysis_surface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf81c4c-bb53-4d96-8dd9-b89b0d17607e",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Create Temperature and Dewpoint DataArrays</p>\n",
    "    Using the information from the previous activity and following the demonstration in the previous cell, create <code>temp</code> and <code>dewp</code> variables that represent the surface temperature and dewpoint, respectively. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "instrumental-monthly",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Create Temperature and Dewpoint DataArrays\n",
    "\n",
    "temp = \n",
    "dewp = "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brave-calculation",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Calculate theta_e</p>\n",
    "    Now, calculate equivalent potential temperature using the  <a href =\"https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.html\" target=\"blank\">metpy.calc documentation</a>. Create a variable called <code>theta_e</code> to represent the equivalent potential temperature field.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "veterinary-dance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Calculate theta_e\n",
    "\n",
    "\n",
    "theta_e = "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54be2ee2-73b7-476e-a408-e27f2d082563",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Challenge: Calculate another variable</p>\n",
    "    Locate another derived variable in the metpy.calc documentation that takes in any combination of surface pressure, temperature, or dewpoint, then calculate it as a new variable. Optionally, plot it using any plotting tool to visualize the output.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "center-jones",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHALLENGE: Calculate another variable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b002f402-e106-452b-805f-3ac3d5c0d313",
   "metadata": {},
   "source": [
    "Now, smooth the theta_e array using a gaussian filter. This will improve the readability of the final plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "quiet-biotechnology",
   "metadata": {},
   "outputs": [],
   "source": [
    "# smooth the theta_e array\n",
    "theta_e = mpcalc.smooth_gaussian(theta_e, n=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infrared-setting",
   "metadata": {},
   "source": [
    "We can use MetPy shortcuts to create a cartopy <a href=\"https://scitools.org.uk/cartopy/docs/latest/crs/index.html#cartopy.crs.CRS\">cartographic reference system</a> for our data. RTMA uses a Lambert Conformal projection, which we will use in our final plot. This is our final data preparation step for the `theta_e` DataArray. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minute-resort",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the crs object for the theta_e array\n",
    "rtma_crs = theta_e.metpy.cartopy_crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-inspection",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "### Step 2c: Visualize Model Output <a class=\"anchor\" id=\"step2c\"></a>\n",
    "\n",
    "The data are now prepared for plotting. Let's use **filled contours** for visualizing the $\\theta_e$ field and add cartopy's built-in `coastlines` class for geographic context.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "finite-hydrogen",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create figure, axes, then use contourf to create filled contours of the theta_e field\n",
    "fig = plt.figure(figsize=[10,20])\n",
    "ax = fig.add_subplot(projection=rtma_crs)\n",
    "ax.contourf(theta_e['x'], theta_e['y'], theta_e, transform=rtma_crs)\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-wales",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Tip</p>\n",
    "    If you are working with xarray DataArrays parsed via <code>metpy.parse_cf</code>, and you aren't sure what your horizontal variables (e.g. x, or lon, or latitude) are named, or you just want a shortcut, you can access these quickly with <code>xarray.metpy.x</code> and <code>xarray.metpy.y</code>.<br><br>For example, these two lines of code are equivalent for DataArrays parsed with <code>metpy.parse.cf</code>:<br><br>\n",
    "    <code>ax.contourf(theta_e['x'], theta_e['y'], theta_e, transform=rtma_crs)</code><br>\n",
    "    <code>ax.contourf(theta_e.metpy.x, theta_e.metpy.y, theta_e, transform=rtma_crs)</code>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b09ec8-2621-4b31-926d-1fbfd61a662e",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Activity: Contour plots</p>\n",
    "    You will complete this activity as a part of a team. You will be assigned a group number and you will document your output on your group's designated slide in the provided presentation.<br>\n",
    "    <ol>\n",
    "    <li>Plot <code>theta_e</code> using the <a href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html\" target=\"blank\">matplotlib <code>contour()</code> plot type</a>.</li>\n",
    "    <li>Use the LambertConformal projection in your axes, the same projection the RTMA dataset uses.</li>\n",
    "    <li>Add coastlines.</li>\n",
    "    <li>Make the contours a single color (<a href=\"https://matplotlib.org/stable/gallery/images_contours_and_fields/contour_demo.html\" target=\"blank\">see examples here</a>).</li>\n",
    "    <li>Add a descriptive title to your plot.</li>\n",
    "    <li>Copy your plot into your group's designated slide at the link provided.</li> \n",
    "        <dl>\n",
    "            <dd>To copy an image from a notebook: Shift + right click on the image, then select Copy image</dd>\n",
    "        </dl>\n",
    "    </ol>\n",
    "    <b>After your group has created the <code>theta_e</code> plot</b>, look to create more contour plots of other RTMA variables you have created in this notebook or any other <a href=\"https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.html\">calculations</a>. Choose any colors, but label your plots with titles that describe the variable(s) plotted. \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "freelance-connectivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ACTIVITY: Contour plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544dff6e-ee37-44ab-bf00-8b17dbd3d114",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Challenge: Make the plot your own</p>\n",
    "   Use the remaining time to stylize your plot to you liking. Use the cell below to experiment with your plot.<br> \n",
    "Some ideas:\n",
    "    <ul>\n",
    "        <li>Stylize the title of the plot, changing the size</li>\n",
    "        <li>Change the colors of the contours</li>\n",
    "        <li>Swap the theta_e contours with another RTMA variable, or add other contours</li>\n",
    "    </ul>\n",
    "Challenges: \n",
    "    <ul>\n",
    "        <li>Make this a multi-panel plot with separate axes for each variable plotted</li>\n",
    "        <li>Add labels to your theta_e contours</li>\n",
    "        <li>Review the MetPy example gallery and add elements to your plot</li>\n",
    "        <dl>\n",
    "            <dd><a href=\"https://unidata.github.io/MetPy/latest/examples/index.html\" target=\"blank\">https://unidata.github.io/MetPy/latest/examples/index.html</a></dd>\n",
    "        </dl>\n",
    "    </ul>\n",
    "Share your final plots with us! <a href=\"https://twitter.com/Metpy\" target=\"blank\">@metpy on Twitter</a> \n",
    "\n",
    "</div>"
   ]
  }
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